Tag: conservation

Lingering questions on the potential to bring sea otters back to Oregon

By Dominique Kone, Masters Student in Marine Resource Management

By now, I’m sure you’re aware of recent interests to reintroduce sea otters to Oregon. To inform this effort, my research focuses on predicting suitable sea otter habitat and investigating the potential ecological effects if sea otters are reintroduced in the future. This information will help managers gain a better understanding of the potential for sea otters to reestablish in Oregon, as well as how Oregon’s ecosystems may change via top-down processes. These analyses will address some sources of uncertainties of this effort, but there are still many more questions researchers could address to further guide this process. Here, I note some lingering questions I’ve come across in the course of conducting my research. This is not a complete list of all questions that could or should be investigated, but they represent some of the most interesting questions I have and others have in Oregon.

Credit: Todd Mcleish

The questions, and our associated knowledge on each of these topics:

Is there enough available prey to support a robust sea otter population in Oregon?

Sea otters require approximately 30% of their own body weight in food every day (Costa 1978, Reidman & Estes 1990). With a large appetite, they not only need to spend most of their time foraging, but require a steady supply of prey to survive. For predators, we assume the presence of suitable habitat is a reliable proxy for prey availability (Redfern et al. 2006). Whereby, quality habitat should supply enough prey to sustain predators at higher trophic levels.

In making these habitat predictions for sea otters, we must also recognize the potential limitations of this “habitat equals prey” paradigm, in that there may be parcels of habitat where prey is unavailable or inaccessible. In Oregon, there could be unknown processes unique to our nearshore ecosystems that would support less prey for sea otters. This possibility highlights the importance of not only understanding how much suitable habitat is available for foraging sea otters, but also how much prey is available in these habitats to sustain a viable otter population in the future. Supplementing these habitat predictions with fishery-independent prey surveys is one way to address this question.

Credit: Suzi Eszterhas via Smithsonian Magazine

How will Oregon’s oceanographic seasonality alter or impact habitat suitability?

Sea otters along the California coast exist in an environment with persistent Giant kelp beds, moderate to low wave intensity, and year-round upwelling regimes. These environmental variables and habitat factors create productive ecosystems that provide quality sea otter habitat and a steady supply of prey; thus, supporting high densities of sea otters. This environment contrasts with the Oregon coast, which is characterized by seasonal changes in bull kelp and wave intensity. Summer months have dense kelp beds, calm surf, and strong upwellings. While winter months have little to no kelp, weak upwellings, and intense wave climates. These seasonal variations raise the question as to how these temporal fluctuations in available habitat could impact the number of sea otters able to survive in Oregon.

In Washington – an environment like Oregon – sea otters exhibit seasonal distribution patterns in response to intensifying wave climates. During calm summer months, sea otters primarily forage along the outer coast, but move into more protected areas, such as the Strait of Juan de Fuca, during winter months (Laidre et al. 2009). If sea otters were reintroduced to Oregon, we may very well observe similar seasonal movement patterns (e.g. dispersal into estuaries), but the degree to which this seasonal redistribution and reduction in foraging habitat could impact sea otter reestablishment and recovery is currently unknown.

Credit: Oregon Coast Aquarium

In the event of a reintroduction, do northern or southern sea otters have a greater capacity to adapt to Oregon environments?

In the early 1970’s, Oregon’s first sea otter translocation effort failed (Jameson et al. 1982). Since then, hypotheses on the potential ecological differences between northern and southern sea otters have been proposed as potential factors of the failed effort, potentially due to different abilities to exploit specific prey species. Studies have demonstrated that northern and southern sea otters have slight morphological differences – northern otters having larger skulls and teeth than southern otters (Wilson et al. 1991). This finding has created the hypothesis that the northern otter’s larger skull and teeth allow it to consume prey with denser exoskeletons, and thereby can exploit a greater diversity of prey species. However, there appears to be a lack of evidence to suggest larger skulls and teeth translate to greater bite force. Based on morphology alone, either sub-species could be just as successful in exploiting different prey species.

A different direction to address questions around adaptability is to look at similarities in habitat and oceanographic characteristics. Sea otters exist along a gradient of habitat types (e.g. kelp forests, estuaries, soft-sediment environments) and oceanographic conditions (e.g. warm-temperature to cooler sub-Arctic waters) (Laidre et al. 2009, Lafferty et al. 2014). Yet, we currently don’t know how well or quickly otters can adapt when they expand into new habitats that differ from ones they are familiar with. Sea otters must be efficient foragers and need to acquire skills that allow them to effectively hunt specific prey species (Estes et al. 2003). Hypothetically, if we take sea otters from rocky environments where they’ve developed foraging skills to hunt sea urchins and abalones, and place them in a soft-sediment environment, how quickly would they develop new foraging skills to exploit soft-sediment prey species? Would they adapt quickly enough to meet their daily prey requirements?

Credit: Eric Risberg/Associated Press via The Columbian

In Oregon, specifically, how might climate change impact sea otters, and how might sea otters mediate climate impacts?

Climate change has been shown to directly impact many species via changes in temperature (Chen et al. 2011). Some species have specific thermal tolerances, in which they can only survive within a specified temperature range (i.e. maximum and minimum). Once the temperature moves out of that range, the species can either move with those shifting water masses, behaviorally adapt or perish (Sunday et al. 2012). It’s unclear if and how changing temperatures will impact sea otters, directly. However, sea otters could still be indirectly affected via impacts to their prey. If prey species in sea otter habitat decline due to changing temperatures, this would reduce available food for otters. Ocean acidification (OA) is another climate-induced process that could indirectly impact sea otters. By creating chemical conditions that make it difficult for species to form shells, OA could decrease the availability of some prey species, as well (Gaylord et al. 2011).

Interestingly, these pathways between sea otters and climate change become more complex when we consider the potentially mediating effects from sea otters. Aquatic plants – such as kelp and seagrass – can reduce the impacts of climate change by absorbing and taking carbon out of the water column (Krause-Jensen & Duarte 2016). This carbon sequestration can then decrease acidic conditions from OA and mediate the negative impacts to shell-forming species. When sea otters catalyze a tropic cascade, in which herbivores are reduced and aquatic plants are restored, they could increase rates of carbon sequestration. While sea otters could be an effective tool against climate impacts, it’s not clear how this predator and catalyst will balance each other out. We first need to investigate the potential magnitude – both temporal and spatial – of these two processes to make any predictions about how sea otters and climate change might interact here in Oregon.

Credit: National Wildlife Federation

In Summary

There are several questions I’ve noted here that warrant further investigation and could be a focus for future research as this potential sea otter reintroduction effort progresses. These are by no means every question that should be addressed, but they do represent topics or themes I have come across several times in my own research or in conversations with other researchers and managers. I think it’s also important to recognize that these questions predominantly relate to the natural sciences and reflect my interest as an ecologist. The number of relevant questions that would inform this effort could grow infinitely large if we expand our disciplines to the social sciences, economics, genetics, so on and so forth. Lastly, these questions highlight the important point that there is still a lot we currently don’t know about (1) the ecology and natural behavior of sea otters, and (2) what a future with sea otters in Oregon might look like. As with any new idea, there will always be more questions than concrete answers, but we – here in the GEMM Lab – are working hard to address the most crucial ones first and provide reliable answers and information wherever we can.

References:

Chen, I., Hill, J. K., Ohlemuller, R., Roy, D. B., and C. D. Thomas. 2011. Rapid range shifts of species associated with high levels of climate warming. Science. 333: 1024-1026.

Costa, D. P. 1978. The ecological energetics, water, and electrolyte balance of the California sea otter (Enhydra lutris). Ph.D. dissertation, University of California, Santa Cruz.

Estes, J. A., Riedman, M. L., Staedler, M. M., Tinker, M. T., and B. E. Lyon. 2003. Individual variation in prey selection by sea otters: patterns, causes and implications. Journal of Animal Ecology. 72: 144-155.

Gaylord et al. 2011. Functional impacts of ocean acidification in an ecologically critical foundation species. Journal of Experimental Biology. 214: 2586-2594.

Jameson, R. J., Kenyon, K. W., Johnson, A. M., and H. M. Wight. 1982. History and status of translocated sea otter populations in North America. Wildlife Society Bulletin. 10(2): 100-107.

Krause-Jensen, D., and C. M. Duarte. 2016. Substantial role of macroalgae in marine carbon sequestration. Nature Geoscience. 9: 737-742.

Lafferty, K. D., and M. T. Tinker. 2014. Sea otters are recolonizing southern California in fits and starts. Ecosphere.5(5).

Laidre, K. L., Jameson, R. J., Gurarie, E., Jeffries, S. J., and H. Allen. 2009. Spatial habitat use patterns of sea otters in coastal Washington. Journal of Marine Mammalogy. 90(4): 906-917.

Redfern et al. 2006. Techniques for cetacean-habitat modeling. Marine Ecology Progress Series. 310: 271-295.

Reidman, M. L. and J. A. Estes. 1990. The sea otter (Enhydra lutris): behavior, ecology, and natural history. United States Department of the Interior, Fish and Wildlife Service, Biological Report. 90: 1-126.

Sunday, J. M., Bates, A. E., and N. K. Dulvy. 2012. Thermal tolerance and the global redistribution of animals. Nature: Climate Change. 2: 686-690.

Wilson, D. E., Bogan, M. A., Brownell, R. L., Burdin, A. M., and M. K. Maminov. 1991. Geographic variation in sea otters, Ehydra lutris. Journal of Mammalogy. 72(1): 22-36.

Eyes from Space: Using Remote Sensing as a Tool to Study the Ecology of Blue Whales

By Christina Garvey, University of Maryland, GEMM Lab REU Intern

It is July 8th and it is my 4th week here in Hatfield as an REU intern for Dr. Leigh Torres. My name is Christina Garvey and this summer I am studying the spatial ecology of blue whales in the South Taranaki Bight, New Zealand. Coming from the east coast, Oregon has given me an experience of a lifetime – the rugged shorelines continue to take my breath away and watching sea lions in Yaquina Bay never gets old. However, working on my first research project has by far been the greatest opportunity and I have learned so much in so little time. When Dr. Torres asked me to contribute to this blog I was unsure of how I would write about my work thus far but I am excited to have the opportunity to share the knowledge I have gained with whoever reads this blog post.

The research project that I will be conducting this summer will use remotely sensed environmental data (information collected from satellites) to predict blue whale distribution in the South Taranaki Bight (STB), New Zealand. Those that have read previous blogs about this research may remember that the STB study area is created by a large indentation or “bight” on the southern end of the Northern Island. Based on multiple lines of evidence, Dr. Leigh Torres hypothesized the presence of an unrecognized blue whale foraging ground in the STB (Torres 2013). Dr. Torres and her team have since proved that blue whales frequent this region year-round; however, the STB is also very industrial making this space-use overlap a conservation concern (Barlow et al. 2018). The increasing presence of marine industrial activity in the STB is expected to put more pressure on blue whales in this region, whom are already vulnerable from the effects of past commercial whaling (Barlow et al. 2018) If you want to read more about blue whales in the STB check out previous blog posts that talk all about it!

Figure 1. A blue whale surfaces in front of a floating production storage and offloading vessel servicing the oil rigs in the South Taranaki Bight. Photo by D. Barlow.
Figure 2. South Taranaki Bight, New Zealand, our study site outlined by the red box. Kahurangi Point (black star) is the site of wind-driven upwelling system.

The possibility of the STB as an important foraging ground for a resident population of blue whales poses management concerns as New Zealand will have to balance industrial growth with the protection and conservation of a critically endangered species. As a result of strong public support, there are political plans to implement a marine protected area (MPA) in the STB for the blue whales. The purpose of our research is to provide scientific knowledge and recommendations that will assist the New Zealand government in the creation of an effective MPA.

In order to create an MPA that would help conserve the blue whale population in the STB, we need to gather a deeper understanding of the relationship between blue whales and this marine environment. One way to gain knowledge of the oceanographic and ecological processes of the ocean is through remote sensing by satellites, which provides accessible and easy to use environmental data. In our study we propose remote sensing as a tool that can be used by managers for the design of MPAs (through spatial and temporal boundaries). Satellite imagery can provide information on sea surface temperature (SST), SST anomaly, as well as net primary productivity (NPP) – which are all measurements that can help describe oceanographic upwelling, a phenomena that is believed to be correlated to the presence of blue whales in the STB region.

Figure 3. The stars of the show: blue whales. A photograph captured from the small boat of one animal fluking up to dive down as another whale surfaces close by. (Photo credit: L. Torres)

Past studies in the STB showed evidence of a large upwelling event that occurs off the coast of Kahurangi Point (Fig. 2), on the northwest tip of the South Island (Shirtcliffe et al. 1990). In order to study the relationship of this upwelling to the distribution of blue whales, I plan to extract remotely sensed data (SST, SST anomaly, & NPP) off the coast of Kahurangi and compare it to data gathered from a centrally located site within the STB, which is close to oil rigs and so is of management interest. I will first study how decreases in sea surface temperature at the site of upwelling (Kahurangi) are related to changes in sea surface temperature at this central site in the STB, while accounting for any time differences between each occurrence. I expect that this relationship will be influenced by the wind patterns, and that there will be changes based on the season. I also predict that drops in temperature will be strongly related to increases in primary productivity, since upwelling brings nutrients important for photosynthesis up to the surface. These dips in SST are also expected to be correlated to blue whale occurrence within the bight, since blue whale prey (krill) eat the phytoplankton produced by the productivity.

Figure 4. A blue whale lunges on an aggregation of krill. UAS piloted by Todd Chandler.

To test the relationships I determine between remotely sensed data at different locations in the STB, I plan to use blue whale observations from marine mammal observers during a seismic survey conducted in 2013, as well as sightings recorded from the 2014, 2016, and 2017 field studies led by Dr. Leigh Torres. By studying the statistical relationships between all of these variables I hope to prove that remote sensing can be used as a tool to study and understand blue whale distribution.

I am very excited about this research, especially because the end goal of creating an MPA really gives me purpose. I feel very lucky to be part of a project that could make a positive impact on the world, if only in just a little corner of New Zealand. In the mean time I’ll be here in Hatfield doing the best I can to help make that happen.

References: 

Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.

Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B (1990) Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal J Mar Freshw Res 24:555–568.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Zooming in: A closer look at bottlenose dolphin distribution patterns off of San Diego, CA

By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Data analysis is often about parsing down data into manageable subsets. My project, which spans 34 years and six study sites along the California coast, requires significant data wrangling before full analysis. As part of a data analysis trial, I first refined my dataset to only the San Diego survey location. I chose this dataset for its standardization and large sample size; the bulk of my sightings, over 4,000 of the 6,136, are from the San Diego survey site where the transect methods were highly standardized. In the next step, I selected explanatory variable datasets that covered the sighting data at similar spatial and temporal resolutions. This small endeavor in analyzing my data was the first big leap into understanding what questions are feasible in terms of variable selection and analysis methods. I developed four major hypotheses for this San Diego site.

The study species: common bottlenose dolphin (Tursiops truncatus) seen along the California coastline in 2015. Image source: Alexa Kownacki.

Hypotheses:

H1: I predict that bottlenose dolphin sightings along the San Diego transect throughout the years 1981-2015 exhibit clustered distribution patterns as a result of the patchy distributions of both the species’ preferred habitats, as well as the social nature of bottlenose dolphins.

H2: I predict there would be higher densities of bottlenose dolphin at higher latitudes spanning 1981-2015 due to prey distributions shifting northward and less human activities in the northerly sections of the transect.

H3: I predict that during warm (positive) El Niño Southern Oscillation (ENSO) months, the dolphin sightings in San Diego would be distributed more northerly, predominantly with prey aggregations historically shifting northward into cooler waters, due to (secondarily) increasing sea surface temperatures.

H4: I predict that along the San Diego coastline, bottlenose dolphin sightings are clustered within two kilometers of the six major lagoons, with no specific preference for any lagoon, because the murky, nutrient-rich waters in the estuarine environments are ideal for prey protection and known for their higher densities of schooling fishes.

Data Description:

The common bottlenose dolphin (Tursiops truncatus) sighting data spans 1981-2015 with a few gap years. Sightings cover all months, but not in all years sampled. The same transect in San Diego was surveyed in a small, rigid-hulled inflatable boat with approximately a two-kilometer observation area (one kilometer surveyed 90 degrees to starboard and port of the bow).

I wanted to see if there were changes in dolphin distribution by latitude and, if so, whether those changes had a relationship to ENSO cycles and/or distances to lagoons. For ENSO data, I used the NOAA database that provides positive, neutral, and negative indices (1, 0, and -1, respectively) by each month of each year. I matched these ENSO data to my month-date information of dolphin sighting data. Distance from each lagoon was calculated for each sighting.

Figure 1. Map representing the San Diego transect, represented with a light blue line inside of a one-kilometer buffered “sighting zone” in pale yellow. The dark pink shapes are dolphin sightings from 1981-2015, although some are stacked on each other and cannot be differentiated. The lagoons, ranging in size, are color-coded. The transect line runs from the breakwaters of Mission Bay, CA to Oceanside Harbor, CA.

Results: 

H1: True, dolphins are clustered and do not have a uniform distribution across this area. Spatial analysis indicated a less than a 1% likelihood that this clustered pattern could be the result of random chance (Fig. 1, z-score = -127.16, p-value < 0.0001). It is well-known that schooling fishes have a patchy distribution, which could influence the clustered distribution of their dolphin predators. In addition, bottlenose dolphins are highly social and although pods change in composition of individuals, the dolphins do usually transit, feed, and socialize in small groups.

Figure 2. Summary from the Average Nearest Neighbor calculation in ArcMap 10.6 displaying that bottlenose dolphin sightings in San Diego are highly clustered. When the z-score, which corresponds to different colors on the graphic above, is strongly negative (< -2.58), in this case dark blue, it indicates clustering. Because the p-value is very small, in this case, much less than 0.01, these results of clustering are strongly significant.

H2: False, dolphins do not occur at higher densities in the higher latitudes of the San Diego study site. The sightings are more clumped towards the lower latitudes overall (p < 2e-16), possibly due to habitat preference. The sightings are closer to beaches with higher human densities and human-related activities near Mission Bay, CA. It should be noted, that just north of the San Diego transect is the Camp Pendleton Marine Base, which conducts frequent military exercises and could deter animals.

Figure 3. Histogram comparing the latitudes with the frequency of dolphin sightings in San Diego, CA. The x-axis represents the latitudinal difference from the most northern part of the transect to each dolphin sighting. Therefore, a small difference would translate to a sighting being in the northern transect areas whereas large differences would translate to sightings being more southerly. This could be read from left to right as most northern to most southern. The y-axis represents the frequency of which those differences are seen, that is, the number of sightings with that amount of latitudinal difference, or essentially location on the transect line. Therefore, you can see there is a peak in the number of sightings towards the southern part of the transect line.

H3: False, during warm (positive) El Niño Southern Oscillation (ENSO) months, the dolphin sightings in San Diego were more southerly. In colder (negative) ENSO months, the dolphins were more northerly. The differences between sighting latitude and ENSO index was significant (p<0.005). Post-hoc analysis indicates that the north-south distribution of dolphin sightings was different during each ENSO state.

Figure 4. Boxplot visualizing distributions of dolphin sightings latitudinal differences and ENSO index, with -1,0, and 1 representing cold, neutral, and warm years, respectively.

H4: True, dolphins are clustered around particular lagoons. Figure 5 illustrates how dolphin sightings nearest to Lagoon 6 (the San Dieguito Lagoon) are always within 0.03 decimal degrees. Because of how these data are formatted, decimal degrees is the easiest way to measure change in distance (in this case, the difference in latitude). In comparison, dolphins at Lagoon 5 (Los Penasquitos Lagoon) are distributed across distances, with the most sightings further from the lagoon.

Figure 5. Bar plot displaying the different distances from dolphin sighting location to the nearest lagoon in San Diego in decimal degrees. Note: Lagoon 4 is south of the study site and therefore was never the nearest lagoon.

I found a significant difference between distance to nearest lagoon in different ENSO index categories (p < 2.55e-9): there is a significant difference in distance to nearest lagoon between neutral and negative values and positive and neutral years. Therefore, I hypothesize that in neutral ENSO months compared to positive and negative ENSO months, prey distributions are changing. This is one possible hypothesis for the significant difference in lagoon preference based on the monthly ENSO index. Using a violin plot (Fig. 6), it appears that Lagoon 5, Los Penasquitos Lagoon, has the widest variation of sighting distances in all ENSO index conditions. In neutral years, Lagoon 0, the Buena Vista Lagoon has multiple sightings, when in positive and negative years it had either no sightings or a single sighting. The Buena Vista Lagoon is the most northerly lagoon, which may indicate that in neutral ENSO months, dolphin pods are more northerly in their distribution.

Figure 6. Violin plot illustrating the distance from lagoons of dolphin sightings under different ENSO conditions. There are three major groups based on ENSO index: “-1” representing cold years, “0” representing neutral years, and “1” representing warm years. On the x-axis are lagoon IDs and on the y-axis is the distance to the nearest lagoon in decimal degrees. The wider the shapes, the more sightings, therefore Lagoon 6 has many sightings within a very small distance compared to Lagoon 5 where sightings are widely dispersed at greater distances.

 

Bottlenose dolphins foraging in a small group along the California coast in 2015. Image source: Alexa Kownacki.

Takeaways to science and management: 

Bottlenose dolphins have a clustered distribution which seems to be related to ENSO monthly indices, and likely, their social structures. From these data, neutral ENSO months appear to have something different happening compared to positive and negative months, that is impacting the sighting distributions of bottlenose dolphins off the San Diego coastline. More research needs to be conducted to determine what is different about neutral months and how this may impact this dolphin population. On a finer scale, the six lagoons in San Diego appear to have a spatial relationship with dolphin sightings. These lagoons may provide critical habitat for bottlenose dolphins and/or for their preferred prey either by protecting the animals or by providing nutrients. Different lagoons may have different spans of impact, that is, some lagoons may have wider outflows that create larger nutrient plumes.

Other than the Marine Mammal Protection Act and small protected zones, there are no safeguards in place for these dolphins, whose population hovers around 500 individuals. Therefore, specific coastal areas surrounding lagoons that are more vulnerable to habitat loss, habitat degradation, and/or are more frequented by dolphins, may want greater protection added at a local, state, or federal level. For example, the Batiquitos and San Dieguito Lagoons already contain some Marine Conservation Areas with No-Take Zones within their reach. The city of San Diego and the state of California need better ways to assess the coastlines in their jurisdictions and how protecting the marine, estuarine, and terrestrial environments near and encompassing the coastlines impacts the greater ecosystem.

This dive into my data was an excellent lesson in spatial scaling with regards to parsing down my data to a single study site and in matching my existing data sets to other data that could help answer my hypotheses. Originally, I underestimated the robustness of my data. At first, I hesitated when considering reducing the dolphin sighting data to only include San Diego because I was concerned that I would not be able to do the statistical analyses. However, these concerns were unfounded. My results are strongly significant and provide great insight into my questions about my data. Now, I can further apply these preliminary results and explore both finer and broader scale resolutions, such as using the more precise ENSO index values and finding ways to compare offshore bottlenose dolphin sighting distributions.

Our GEM(M), Ruby, is back in action!

By Lisa Hildebrand, MSc student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Every season, or significant period of time, usually has a distinct event that marks its beginning. For example, even though winter officially begins when the winter solstice occurs sometime between December 20 and December 23, many people often associate the first snowfall as the real start of winter. To mark the beginning of schooling, when children start 1stgrade in Germany (which is where I’m from), they receive something called a “Zuckertüte”, which translated means “sugar bag”. It is a large (sometimes as large as the child) cone-shaped container made of cardboard filled with toys, chocolates, sweets, school supplies and various other treats topped with a large bow.

Receiving my Zuckertüte in August of 2001 before starting 1st grade. Source: Ines Hildebrand.

I still remember (and even have) mine – it was almost as tall as I was, had a large Barbie printed on it (and a real one sitting on top of it) and was bright pink. And of course, while at a movie theatre, once the lights dim completely and the curtain surrounding the screen opens just a little further, members of the audience stop chit-chatting or sending text messages, everyone quietens down and puts their devices away – the film is about to start. There are hundreds upon thousands of examples like these – moments, events, days that mark the start of something.

In the past, the beginning of summer has always been tied to two things for me: the end of school and the chance to be outside in the sun for many hours and days. This reality has changed slightly since moving to Oregon. While I don’t technically have any classes during the summer, the work definitely won’t stop. There are still dozens of papers to read, samples to run in the lab, and data points to plot. For anyone from Oregon or the Pacific Northwest (PNW), it’s pretty well known that the weather can be a little unpredictable and variable, meaning that summer might not always be filled with sunny days. Despite somewhat losing these two “summer markers”, I have found a new event to mark the beginning of summer – the arrival of the gray whales.

Their propensity for coastal waters and near-shore feeding is part of what makes gray whales so unique and arguably “easier” to study than some other baleen whale species. Image captured under NOAA/NMFS permit #21678. Source: Leigh Torres.

 

It’s official – the gray whale field season is upon us! As many of you may already know, the GEMM Lab has two active gray whale research projects: investigating the impacts of ocean noise on gray whale physiology and exploring potential individual foraging specialization among the Pacific Coast Feeding Group (PCFG) gray whales. Both projects involve field work, with the former operating out of Newport and the latter taking place in Port Orford, both collecting photographs and a variety of samples and tracklines to study the PCFG, which is a sub-group of the larger Eastern North Pacific (ENP) population. June 1st is the widely accepted “cut-off date” for the PCFG whales, whereby gray whales seen after June 1st along the PNW coastline (specifically northern California, Oregon, Washington and British Columbia) are considered members of the PCFG. While this date is not the only qualifying factor for an individual to be considered a PCFG member, it is a good general rule of thumb. Since last week happened to be the first week of June, PI Leigh Torres, field technician Todd Chandler and myself launched out onto the Pacific Ocean in our trusty RHIB Ruby twice looking for gray whales, and it sure was a successful start to the season!

Even though I have done small boat-based field work before, every project and field team operates a little differently, which is why I was a little nervous at first. There are a lot of components to the Newport-based project as Leigh & co. assess gray whale physiology by collecting fecal samples, drone imagery and taking photographs, observing behavior patterns, as well as assessing local prey through GoPro footage and light traps. I wasn’t worried about the prey components of the research, since there is plenty of prey sampling involved in my Port Orford research, however I was worried about the whale side of things. I wasn’t sure whether I would be able to catch the drone as it returned back home to Ruby, fearing I might fumble and let it slip through my fingers. I also experienced slight déjà vu when handling the net we use to collect the fecal samples as I was forced to think back to some previous field work that involved collecting a biopsy dart with a net as well. During that project, I had somehow managed to get the end of the net stuck in the back of the boat and as I tried to scoop up the biopsy dart with the net-end, the pole became more and more stuck while the water kept dragging the net-end down and eventually the pole ended up snapping in my hands. On top of all this anxiety and work, trying to find your footing in a small RHIB like Ruby packed with lots of gear and a good amount of swell doesn’t make any of those tasks any easier.

However, as it turned out, none of my fears came to fruition. As soon as Todd fired up Ruby’s engine and we whizzed out and under the Newport bridge, I felt exhilarated. I love field work and was so excited to be out on the water again. During the two days I was able to observe multiple individuals of a species of whale that I find unique and fascinating.

Markings and pigmentation on the flukes are also unique to individuals and allow us to perform photo identification to track individuals over months and years. Image captured under NOAA/NMFS permit #21678. Source: Leigh Torres.

I felt back in my natural element and working with Leigh and Todd was rewarding and fun, as I have so much to learn from their years of experience and natural talent in the field dealing with stressful situations and juggling multiple components and gear. Even though I wasn’t out there collecting data for my own project, some of my observations did get me thinking about what I hope to focus on in my thesis – individualization. It is always interesting to see how differently whales will behave, whether due to the substrate we find them over, the water depths we find them in, or what their surfacing patterns are like. Although I still have six weeks to go until my field season starts and feel lucky to have the opportunity to help Leigh and Todd with the Newport field work, I am already looking forward to getting down to Port Orford in mid-July and starting the fifth consecutive gray whale field season down there.

But back to Newport – over the course of two days, we were able to deploy and retrieve one light trap to collect zooplankton, collect two fecal samples, perform two GoPro drops, fly the drone three times, and take hundreds of photos of whales. Leigh and Todd were both glad to be reunited with an old friend while I felt lucky to be able to meet such a famous lady – Scarback. A whale with a long sighting history not just for the GEMM Lab but for various researchers along the coast that study this population. Scarback is well-known (and easily identified) by the large concave injury on her back that is covered in whale lice, or cyamids. While there are stories about how Scarback’s wound came to be, it is not known for sure how she was injured. However, what researchers do know is that the wound has not stopped this female from reproducing and successfully raising several calves over her lifetime. After hearing her story from Leigh, I wasn’t surprised that both she and Todd were so thrilled to get both a fecal sample and a drone flight from her early in the season. The two days weren’t all rosy; most of day 1 was shrouded in a cloud of mist resulting in a thin but continuous layer of moisture forming on our clothes, while on day 2 we battled with some pretty big swells (up to 6 feet tall) and in typical Oregon coast style we were victims of a sudden downpour for about 10 minutes. We had some excellent sightings and some not-so-excellent sightings. Sightings where we had four whales surrounding our boat at the same time and sightings where we couldn’t re-locate a whale that had popped up right next to us. It happens.

 

A local celebrity – Scarback. Image captured under NOAA/NMFS permit #21678. Source: Lisa Hildebrand.

 

An ecstatic Lisa with wild hair standing in the bow pulpit of Ruby camera at the ready. Source: Leigh Torres.

Field work is certainly one of my favorite things in the world. The smell of the salt, the rustling of cereal bar wrappers, the whipping of hair, the perpetual rosy noses and cheeks no matter how many times you apply and re-apply sunscreen, the awkward hilarity of clambering onto the back of the boat where the engine is housed to take a potty break, the whooshing sound of a blow, the sometimes gentle and sometimes aggressive rocking of the boat, the realization that you haven’t had water in four hours only to chug half of your water in a few seconds, the waft of peanut butter and jelly sandwiches, the circular footprint where a whale has just gracefully dipped beneath the surface slipping away from view. I don’t think I will ever tire of any of those things.

 

 

Should scientists engage in advocacy?

By Dominique Kone, Masters Student in Marine Resource Management

Should scientists engage in advocacy? This question is one of the most debated topics in conservation and natural resource management. Some experts firmly oppose researchers advocating for policy decisions because such actions potentially threaten the credibility of their science. While others argue that with environmental issues becoming more complex, society would benefit from hearing scientists’ opinions and preferences on proposed actions. While both arguments are valid, we must recognize the answer to this question may never be a universal yes or no. As an early-career scientist, I’d like to share some of my observations and thoughts on this topic, and help continue this dialogue on the appropriateness of scientists exercising advocacy.

Policymakers are tasked with making decisions that determine how species and natural resources are managed, and subsequently affect and impact society. Scientists commonly play an integral role in these policy decisions, by providing policymakers with reliable and accurate information so they can make better-informed decisions. Examples include using stock assessments to set fishing limits, incorporating the regeneration capacity of forests into the timing of timber harvest, or considering the distribution of blue whales in permitting seafloor mining projects. Importantly, informing policy with science is very different from scientists advocating on policy issues. To understand these nuances, we must first define these terms.

A scientist considering engaging in policy advocacy. Source: Karen Brey.

According to Merriam-Webster, informing means “to communicate knowledge to” or “to give information to an authority”. In contrast, advocating means “to support or argue for (a cause, policy, etc.)” (Merriam-Webster 2019). People can inform others by providing information without necessarily advocating for a cause or policy. For many researchers, providing credible science to inform policy decisions is the gold standard. We, as a society, do not take issue with researchers supplying policymakers with reliable information. Rather, pushback arises when researchers step out of their role as informants and attempt to influence or sway policymakers to decide in a particular manner by speaking to values. This is advocacy.

Dr. Robert Lackey is a fisheries & political scientist, and one of the prominent voices on this issue. In his popular 2007 article, he explains that when scientists inform policy while also advocating, a conflict of interest is created (Lackey 2007). To an outsider, it can be difficult to distinguish values from scientific evidence when researchers engage in policy discussions. Are they engaging in these discussions to provide reliable information as an honest scientist, or are they advocating for decisions or policies based on their personal preferences? As a scientist, I like to believe most scientists – in natural resource management and conservation – do not engage in policy decisions for their own benefit, and they truly want to see our resources managed in a responsible and sustainable manner. Yet, I also recognize this belief doesn’t negate the fact that when researchers engage in policy discussions, they could advocate for their personal preferences – whether they do so consciously or subconsciously – which makes identifying these conflicts of interest particularly challenging.

Examples of actions scientists take in conducting and reporting research. Actions on the left represent actions of policy advocacy, those on the right do not, and the center is maybe. This graphic was adapted from a policy advocacy graphic from Scott et al. 2007. Source: Jamie Keyes.

It seems much of the unease with researchers exercising advocacy has to do with a lack in transparency about which role the researcher chooses to play during those policy debates. A simple remedy to this dilemma – as Lackey suggested in his paper – could be to encourage scientists to be completely transparent when they are about to inform versus advocate (Lackey 2007). Yet, for this suggestion to work, it would require complete trust in scientists to (1) verbalize and make known whether they’re informing or advocating, and (2) when they are informing, to provide credible and unbiased information. I’ve only witnessed a few scientists do this without ensuing some skepticism, which unfortunately highlights issues around an emerging mistrust of researchers to provide policy-neutral science. This mistrust threatens the important role scientists have played in policy decisions and the relationships between scientists and policymakers.

While much of this discussion has been focused on how researchers and their science are received by policymakers, researchers engaging in advocacy are also concerned with how they are perceived by their peers within the scientific community. When I ask more-senior researchers about their concerns with engaging in advocacy, losing scientific credibility is typically at or near the top of their lists. Many of them fear that once you start advocating for a position or policy decision (e.g. protected areas, carbon emission reduction, etc.), you become known for that one cause, which opens you up to questions and suspicions on your ability to provide unbiased and objective science. Once your credibility as a scientist comes into question, it could hinder your career.

How it sometimes feels when researchers conduct policy-relevant science. Source: Justin DeFreitas.

Conservation scientists are faced with a unique dilemma. They value both biodiversity conservation and scientific credibility. Yet, in some cases, risk or potential harm to a species or ecosystem may outweigh concerns over damage to their credibility, and therefore, may choose to engage in advocacy to protect that species or ecosystem (Horton 2015). Horton’s explanation raises an important point that researchers taking a hands-off approach to advocacy may not always be warranted, and that a researcher’s decision to engage in advocacy will heavily depend on the issue at hand and the repercussions if the researcher does not advocate their policy preferences. Climate change is a great example, where climate scientists are advocating for the use of their science, recognizing the alternative could mean continued inaction on carbon emission reduction and mitigation. [Note: this is called science advocacy, which is slightly different than advocating personal preferences, but this example helps demonstrate my point.]

To revisit the question – should scientists engage in advocacy? Honestly, I don’t have a clear answer, because there is no clear answer. This topic is one that has so many dimensions beyond the few I mentioned in this blog post. In my opinion, I don’t think researchers should have an always yes or always no stance on advocacy. Nor do I think every researcher needs to agree on this topic. A researcher’s decision to engage in advocacy will all depend on context. When faced with this decision, it might be useful to ask yourself the following questions: (1) How much do policymakers trust me? (2) How will my peers perceive me if I choose to engage? (3) Could I lose scientific credibility if I do engage? And (4) What’s at stake if I don’t make my preferences known? Hopefully, the answers to these sub-questions will help you decide whether you should advocate.

References:

Horton, C. C., Peterson, T. R., Banerjee, P., and M. J. Peterson. 2015. Credibility and advocacy in conservation science. Conservation Biology. 30(1): 23-32.

Lackey, R. T. 2007. Science, Scientists, and Policy Advocacy. Conservation Biology. 21(1): 12-17.

Scott et al. (2007). Policy advocacy in science: prevalence, perspectives, and implications for conservation biologists. Conservation Biology. 21(1): 29-35.

Merriam-Webster. 2019. Retrieved from < https://www.merriam-webster.com/ >

Marine Mammal Observing: Standardization is key

By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

For the past two years, I’ve had the opportunity to be the marine mammal observer aboard the NOAA ship Bell M. Shimada for 10 days in May. Both trips covered transects in the Northern California Current Ecosystem during the same time of year, but things looked very different from my chair on the fly bridge. This trip, in particular, highlighted the importance of standardization, seeing as it was the second replicate of the same area. Other scientists and crew members repeatedly asked me the same questions that made me realize just how important it is to have standards in scientific practices and communicating them.

Northern right whale dolphin porpoising out of the water beside the ship while in transit. May 2019. Image source: Alexa Kownacki

The questions:

  1. What do you actually do here and why are you doing it?
  2. Is this year the same as last year in terms of weather, sightings, and transect locations?
  3. Did you expect to see greater or fewer sightings (number and diversity)?
  4. What is this Beaufort Sea State scale that you keep referring to?

All of these are important scientific questions that influence our hypothesis-testing research, survey methods, expected results, and potential conclusions. Although the entire science party aboard the ship conducted marine science, we all had our own specialties and sometimes only knew the basics, if that, about what the other person was doing. It became a perfect opportunity to share our science and standards across similar, but different fields.

Now, to answer those questions:

  1. a) What do you actually do here and b) why are you doing it?

a) As the only marine mammal observer, I stand watch during favorable weather conditions while the ship is in transit, scanning from 0 to 90 degrees off the starboard side (from the front of the ship to a right angle towards the right side when facing forwards). Meanwhile, an application on an iPad called SeaScribe, records the ship’s exact location every 15 seconds, even when no animal is sighted. This process allows for the collection of absence data, that is, data when no animals are present. The SeaScribe program records the survey lines, along with manual inputs that I add, including weather and observer information. When I spot a marine mammal, I immediately mark an exact location on a hand held GPS, use my binoculars to identify the species, and add information to the sighting on the SeaScribe program, such as species, distance to the sighted animal(s), the degree (angle) to the sighting, number of animals in a group, behavior, and direction if traveling.

b) Marine mammal observing serves many different purposes. In this case, observing collects information about what species are where at what time. By piggy-backing on these large-scale, offshore oceanographic NOAA surveys, we have the unique opportunity to survey along standardized transect lines during different times of the year. From replicate survey data, we can start to form an idea of which species use which areas and what oceanographic conditions may impact species distributions. Currently there is not much consistent marine mammal data collected over these offshore areas between Northern California and Washington State, so our work is aiming to fill this knowledge gap.

Alexa observing on the R/V Shimada in May 2019, all bundled up. Image Source: Alexa Kownacki
  1. What is this Beaufort Sea State scale that you keep referring to?

Great question! It took me a while to realize that this standard measuring tool to estimate wind speeds and sea conditions, is not commonly recognized even among other sea-goers. The Beaufort Sea State, or BSS, uses an empirical scale that ranges from 0-12 with 0 being no wind and calm seas, to 12 being hurricane-force winds with 45+ ft seas. It is frequently referenced by scientists in oceanography, marine science, and climate science as a universally-understood metric. The BSS was created in 1805 by Francis Beaufort, a hydrographer in the Royal Navy, to standardize weather conditions across the fleet of vessels. By the mid-1850s, the BSS was standardized to non-naval use for sailing vessels, and in 1916, expanded to include information specific to the seas and not the sails1. We in the marine mammal observation field constantly collect BSS information while on survey to measure the quality of survey conditions that may impact our observations. BSS data allows us to measure the extent of our survey range, both in the distance that we are likely to sight animals and also the likelihood of sighting anything. Therefore, the BSS scale gives us an important indication of how much absence data we have collected, in addition to presence data.

A description of the Beaufort Sea State Scale. Image source: National Weather Service.

 

  1. Is this year the same as last year in terms of weather, sightings, and transect locations?

The short answer is no. Observed differences in marine mammal sightings in terms of both species diversity and number of animals between years can be normal. There are many potential explanatory variables, from differences in currents, upwelling strength, El Nino index levels, water temperatures, or, what was obvious in this case: sighting conditions. The weather in May 2019 varied greatly from that in May 2018. Last year, I observed for nearly every day because the Beaufort Sea State (BSS) was frequently less than a four. However, this year, more often than not, the BSS greater than or equal to five. A BSS of 5 equates to approximately 17-21 knots of breeze with 6-foot waves and the water appears to have many “white horses” or pronounced white caps with sea spray. Additionally, mechanical issue with winches delayed and altered our transect locations. Therefore, although multiple transects from May 2018 were also surveyed during May 2019, there were a few lines that do not have data for both cruises.

May 2018 with a BSS 1
May 2019 with a BSS 6

 

 

 

 

 

  1. Did you expect to see greater or fewer sightings (number and diversity)?

Knowing that I had less favorable sighting conditions and less amount of effort observing this year, it is not surprising that I observed fewer marine mammals in total count and in species diversity. Even less surprising is that on the day with the best weather, where the BSS was less than a five, I recorded the most sightings with the highest species count. May 2018 felt a bit like a tropical vacation because we had surprisingly sunny days with mild winds, and during May 2019 we had some rough seas with gale force winds. Additionally, as an observer, I need to remove as much bias as possible. So, yes, I had hoped to see beaked whales or orca like I did in May 2018, but I was still pleasantly surprised when I spotted fin whales feeding in May 2019.

Marine Mammal Species Number of Sightings
May 2018 May 2019
Humpback whale 31 6
Northern right whale dolphin 1 2
Pacific white-sided dolphin 3 6
UNID beaked whale 1 0
Cuvier’s beaked whale 1 0
Gray whale 4 1
Minke whale 1 1
Fin whale 4 1
Blue whale 1 0
Transient killer whale 1 0
Dall’s porpoise 2 0
Northern fur seal 1 0
California sea lion 0 1
Pacific white-sided dolphin. Image source: Alexa Kownacki

Standardization is a common theme. Observing between years on standard transects, at set speeds, in different conditions using standardized tools is critical to collecting high quality data that is comparable across different periods. Scientists constantly think about quality control. We look for trends and patterns, similarities and differences, but none of those could be understood without having standard metrics.

The entire science party aboard the R/V Shimada in May 2019, including a marine mammal scientist, phytoplankton scientists, zooplankton scientists, and fisheries scientists, and oceanographers. Image Source: Alexa Kownacki

Literature Cited:

1Oliver, John E. (2005). Encyclopedia of world climatology. Springer.

 

 

Knowing me, knowing you: the fate of the toninha, a small dolphin endemic to the Western South Atlantic

By Salvatore Siciliano (1,2)

(1) Laboratório de Enterobactérias, Oswaldo Cruz Institute/Fiocruz, Rio de Janeiro, Brazil
(2) Grupo de Estudos de Mamíferos Marinhos da Região dos Lagos (GEMM-Lagos)

 

 

Background information on Pontoporia blainvillei

The toninha (Pontoporia blainvillei) as it is called in Brazil, or franciscana (Fig.01), is a small dolphin endemic to coastal waters of southeastern and southern Brazil, Uruguay and Argentina. It is the only representative of an ancient lineage of odontocetes, once widely spread over the Pacific and Atlantic oceans. Toninhas occur in waters shallower than 30 m and present a discontinuous distribution from Itaúnas, Brazil (18º 25’S) to Golfo San Matías, Argentina (42º 10’S). The species is considered one of the most threatened small cetaceans in South America due to high, and possibly unsustainable, bycatch levels as well as increasing habitat degradation. Incidental catches in fishing gear, especially gillnets and trammel nets, have been reported along most of the species’ range since at least the 1940s. Other rapidly-increasing conservation issues of significant importance for the franciscana in this region include: (1) habitat degradation, (2) underwater noise, (3) chemical pollution from industrial and urban wastewater, (4) activities related to the exploration and production of oil and gas, and (5) vessel traffic. P. blainvilleiis currently listed as ‘Vulnerable’ in the IUCN Red List of Threatened Species and ‘Critically Endangered’ by the Brazilian Government.

 

Figure 01: A young Pontoporia blainvillei incidentally caught in gillnets set off the northern coast of the state of Rio de Janeiro, Brazil (December 2011).

 

In order to guide conservation and management actions on a regional basis, the franciscana range was divided into four zones, known as ‘Franciscana Management Areas’ (FMAs), in the early 2000s. FMA I includes Espírito Santo (ES) and northern Rio de Janeiro (RJ), states located in southeastern Brazil. FMA II corresponds to southern RJ, São Paulo (SP), Paraná (PR) and northern Santa Catarina (SC) states, in southeastern and southern Brazil. FMA III encompasses southern SC and Rio Grande do Sul (RS) states, in southern Brazil, in addition to Uruguay. The last FMA, the FMA IV, corresponds to the Argentina coast (Fig.02).

The absence of stranded or incidentally killed animals indicates a gap of approximately 320 km in the franciscana distribution between northern and southern RJ. This gap separates the southern border of FMA I and the northern border of FMA II.

 

Figure 02: The FMA areas (in blue) in P. blainvillei distribution range, and the gaps (in white) in toninha distribution along the Northern limit of its distribution in Southeastern Brazil.

 

The toninha is usually very shy and, for this reason, quite difficult to be seen in the wild. More recently, researchers and citizen science projects have succeeded in obtaining very nice pictures of these animals (Fig.03), which are aiding in elucidating the species mysterious behavior, feeding activity and their preferred habitat conditions.

Figure 03: Toninhas in their natural environment along shallow waters off northern São Paulo state, in the summer of 2019. Photo courtesy of Júlio Cardoso, Baleia à Vista Project.

 

Figure 04: Aerial view of the Restinga de Jurubatiba National Park and its adjacent waters, the main toninha habitat along the northern coast of Rio de Janeiro. Photo by Salvatore Siciliano (November 2017).

 

Threats to P. blainvillei along the Brazilian coast

Gillnets are still the main cause of toninha mortality along its entire range. They can be used at the surface or placed at the bottom of the ocean to catch fish, but these nets also entangle this small dolphin (Fig.05, Fig.06).

Figure 05: Gillnets, used at the surface or placed at the bottom of the ocean.

 

Figure 06: Data on gillnet incidental captures of toninhas (Pontoporia blainvillei) along the northern coast of Rio de Janeiro state collected since1988. Note the concentration of records in the Macaé – Quissamã and Cabo de São Thomé areas, adjacent to the Restinga de Jurubatiba National Park. Data on captures come from Prof. Ana Paula M. Di Beneditto/CBB/LCA/UENF.

 

Toninhas also face other threats along the Brazilian coast, including environmental chemical contamination by metals and persistent organic pollutants. These pollutants are persistent in the aquatic ecosystem and may accumulate and magnify throughout the tropic chain, causing deleterious effects in the aquatic fauna. Recently, an ecotoxicological assessment from our research group (GEMM-Lagos/Fiocruz) verified, for the first time, significant intracellular concentrations of several toxic metals, such as Hg and Pb (Fig.07), in P. blainvillei individuals sampled along the coast of the Rio de Janeiro state.

 

Figure 07: Novel HPLC-ICP-MS data on intracellular Pb and Hg in P. blainvillei liver (L), muscle (M) and kidney (K) samples from stranded individuals sampled off the coast of Rio de Janeiro, Brazil.

 

The monitoring of the contaminant levels in toninhas will potentially aid in conservation efforts, as we can identify which metals are of the most concern, because the intracellular presence of toxic metals indicates high bioavailability, probably leading to deleterious effects.

 

Conservation Efforts

What is a Whale Heritage Site (WHS) and why we are proposing ‘Mosaic Jurubatiba’ as a WHS?

Situated on the Northern coast of Rio de Janeiro state, Brazil, the Jurubatiba region (Fig.04; Fig.08) is now a Candidate Whale Heritage Site (WHS). The area has been termed ‘Mosaic Jurubatiba’ as the candidate site includes not only the Jurubatiba National Park, but also encompasses other significant sites for conservation along the central-north coast that lie across three municipalities: Macaé, Carapebus and Quissamã (Fig.08).

Figure 08: Proposed extension of the Jurubatiba National Park to the adjacent waters, home of a vigorous population of P. blainvillei.
Legend: green – Jurubatiba National Park; red – new terrestrial limit; yellow – new marine limit.

 

The location provides habitat to a diversity of wildlife. When considering cetaceans, the most regularly seen individuals are the humpback whales, the Guiana dolphins and the toninhas. This is an important site since it is part of the migration route of humpback whales from their breeding and calving grounds, in warm tropical waters, to their feeding grounds, in Antarctica. In addition, this locality is a significant habitat for the toninha, a restricted range species, and the Guiana dolphin, a data deficient species and, therefore, of great concern. The importance of the site becoming a fully accredited WHS is, therefore, evident to further conserve these species and their habitats.

There is a significant amount of active conservation in the Jurubatiba National Park. The Park is the first to exclusively comprise the Restinga ecosystem. Researchers worked alongside authorities and large organizations, such as IBAMA (Brazilian Ministry of Environment and the federal government), to achieve its national park status.

Figure 09: Outreach material produced for the campaign ‘Mosaic Jurubatiba’ to promote education and conservation of the Toninha.

 

In Quissamã, warning signs were placed along the beaches to alert the population of the importance of the coastal waters as habitat for dolphin species, especially the toninha. This type of cooperation and support of the government and other authorities will aid the candidate site to achieve a full status of WHS.

The long-term goals of the candidate site are to influence the transition away from fishing as a livelihood and to instead embrace the use of responsible tourism to make a living.

 

For more information on Whale Heritage Sites around the world, visit:

http://worldcetaceanalliance.org/

http://whaleheritagesites.org/candidate-site-jurubatiba/

 

For more information on the GEMM-Lagos Project:

contact:gemmlagos@gmail.com

visit their Instagram: toninha_cade_vc

 

Here you can also find a list of some of the Salvatore Siciliano’s publications on Pontoporia blainvillei:

  • Siciliano S, de Moura JF, Tavares DC, Kehrig HA, Hauser-Davis RA, Moreira I, Lavandier R, Lemos LS, EMin-Lima R, Quinete N. 2018. Legacy Contamination in Estuarine Dolphin Species From the South American Coast. In book: Marine Mammal Ecotoxicology. Eds. Fossi MC, Panti C. Publisher: Academic Press.
  • Baptista G, Kehrig HA, Di Beneditto APM, Hauser-Davis RA, Almeida MG, Rezende CE, Siciliano S, de Moura JF and Moreira I. 2016. Mercury, selenium and stable isotopes in four small cetaceans from the Southeastern Brazilian coast: Influence of feeding strategy. Environmental Pollution 218:1298-1307.
  • Frainer G, Siciliano S, Tavares DC. 2016. Franciscana calls for help: the short and long-term effects of Mariana’s disaster on small cetaceans of South-eastern Brazil. International Whaling Commission SC/66b/SM/04. Bled, Slovenia.
  • Lavandier R, Arêas J, Quinete N, de Moura JF, Taniguchi S, Montone RC, Siciliano S, Moreira I. 2015. PCB and PBDE levels in a highly threatened dolphin species from the Southeastern Brazilian coast. Environmental Pollution 208.
  • Lemos LS, de Moura JF, Hauser-Davis RA, de Campos RC, Siciliano S. 2013. Small cetaceans found stranded or accidentally captured in southeastern Brazil: Bioindicators of essential and non-essential trace elements in the environment. Ecotoxicology and Environmental Safety 97:166-175.
  • de Moura JF, Rodrigues ES, Sholl TGC, Siciliano S. 2009. Franciscana dolphin (Pontoporia blainvillei) on the north-east coast of Rio de Janeiro State, Brazil, recorded during a long-term monitoring programme. Marine Biodiversity Records 2:e66.

 

 

Highlights from the 11th Sea Otter Conservation Workshop

By Dominique Kone, Masters Student in Marine Resource Management

I recently attended and presented at the 11th biennial Sea Otter Conservation Workshop (the Workshop), hosted by the Seattle Aquarium. As the largest sea otter-focused meeting in the world, the Workshop brought together dozens of scientists, managers, and conservationists to share important information and research on sea otter conservation issues. Being new to this community, this was my first time attending the Workshop, and I had the privilege of meeting some of the most influential sea otter experts in the world. Here, I recount some of my highlights from the Workshop and discuss the importance of this meeting to the continued conservation and management of global sea otter populations.

Source: The Seattle Aquarium.

Sea otters represent one of the most successful species recovery stories in history. After facing near extinction at the close of the Maritime Fur Trade in 1911 (Kenyon 1969), they have made an impressive comeback due to intense conservation efforts. The species is no longer in such dire conditions, but some distinct populations are still considered at-risk due to their small numbers and persistent threats, such as oil spills or disease. We still have a ways to go until global sea otter populations are recovered, and collaboration across disciplines is needed for continued progress.

The Workshop provided the perfect means for this collaboration and sharing of information. Attendees were a mixture of scientists, managers, advocacy groups, zoos and aquarium staff, and graduate students. Presentations spanned a range of disciplines, including ecology, physiology, genetics, and animal husbandry, to name a few. On the first day of the Workshop, most presentations focused on sea otter ecology and management. The plenary speaker – Dr. Jim Estes (retired ecologist and University of California, Santa Cruz professor) – noted that one of the reasons we’ve had such success in sea otter recovery is due to our vast knowledge of their natural history and behavior. Much of this progress can be attributed to seminal work, such as Keyon’s 1969 report, which provides an extensive synthesis of several sea otter ecological and behavioral studies (Kenyon 1969). Beginning in the 1970’s, several other ecologists – such as David Duggins, Jim Bodkin, Tim Tinker, and Jim himself – expanded this understanding to complex trophic cascades, individual diet specialization, and population demographics.

Jim Estes and Tim Tinker. Source: Jim Estes.

These ecological studies have played an integral role in sea otter conservation, but other disciplines were and continue to be just as important. As the Workshop continued into the second and third days, presentations shifted their focus to physiology, veterinary medicine, and animal husbandry. Two of these speakers – who have played pivotal roles in these areas – are Dr. Melissa Miller (veterinarian specialist and pathologist with the California Department of Fish & Wildlife) and Dr. Mike Murray (director of veterinary services at the Monterey Bay Aquarium). Dr. Miller presented her years of work on understanding causes of mortality in wild southern sea otters in California. Her research showed that shark predation is a large source of mortality in the southern stock, but cardiac arrest, which has gained less attention, is also a large contributing factor.

Dr. Murray discussed his practice of caring for and studying the biology of captive sea otters. He provided an overview of some of the routine procedures (i.e. full body exams, oral surgeries, and radio transmitter implantation) his team conducts to assess and treat stranded wild otters, so they can be returned to the wild. Both presenters demonstrated how advances in veterinary medicine have helped us better understand the multitude of threats to sea otters in the wild, and what interventive measures can be taken to recover sick or injured otters so they can contribute to wild population recovery. By understanding how these threats are impacting sea otter health on an individual level, we can be better equipped to prevent population-wide consequences.

Dr. Melissa Miller conducting a sea otter necropsy. Source: California Department of Fish & Game.

Throughout the entire Workshop, experts with decades of experience presented their work. Yet, one of the most encouraging aspects of this meeting was that several graduate students also presented their research, including myself. In a way, listening to presenters both early and late in their careers gave us a glimpse into the past and future of sea otter conservation. Much of the work currently being conducted by graduate students addresses some of the most pressing and emerging issues (e.g. shark predation, plastic pollution, and diseases) in this field, but also builds off the great knowledge base acquired by many of those at the Workshop.

Perhaps even more encouraging was the level of collaboration and mentorship between graduate students and seasoned experts. Included in almost every graduate student’s acknowledgement section of their presentations, were the names of several Workshop attendees who either advised them or provided guidance on their research. These presentations were often followed up with further meetings between students and their mentors. These types of interactions really demonstrated how invested the sea otter community is in fostering the next generation of leaders in this field. This “passing of the mantel” is imperative to maintain knowledge between generations and to continue to make progress in sea otter conservation. As a graduate student, I greatly appreciated getting the opportunity to interact with and gain advice from many of these researchers, whom I’ve only read about in articles.

Source: Bay Nature.

To summarize my experience, it became clear how important this Workshop was to the broader sea otter conservation community. The Workshop provided the perfect venue for collaboration amongst experts, as well as mentorship of upcoming leaders in the field. It’s important to recognize the great progress and strides the community has made already in understanding the complex lives of sea otters. Sea otters have not recovered everywhere. Therefore, we need to continue to acquire knowledge across all disciplines if we are to make progress in the future, especially as new threats and issues emerge. It will take a village.

Literature Cited:

Kenyon, K. W. 1969. The sea otter in the eastern Pacific Ocean. North American Fauna. 68. 352pp.

Photogrammetry Insights

By Leila Lemos, PhD Candidate, Fisheries and Wildlife Department, Oregon State University

After three years of fieldwork and analyzing a large dataset, it is time to finally start compiling the results, create plots and see what the trends are. The first dataset I am analyzing is the photogrammetry data (more on our photogrammetry method here), which so far has been full of unexpected results.

Our first big expectation was to find a noticeable intra-year variation. Gray whales spend their winter in the warm waters of Baja California, Mexico, period while they are fasting. In the spring, they perform a big migration to higher latitudes. Only when they reach their summer feeding grounds, that extends from Northern California to the Bering and Chukchi seas, Alaska, do they start feeding and gaining enough calories to support their migration back to Mexico and subsequent fasting period.

 

Northeastern gray whale migration route along the NE Pacific Ocean.
Source: https://journeynorth.org/tm/gwhale/annual/map.html

 

Thus, we expected to see whales arriving along the Oregon coast with a skinny body condition that would gradually improve over the months, during the feeding season. Some exceptions are reasonable, such as a lactating mother or a debilitated individual. However, datasets can be more complex than we expect most of the times, and many variables can influence the results. Our photogrammetry dataset is no different!

In addition, I need to decide what are the best plots to display the results and how to make them. For years now I’ve been hearing about the wonders of R, but I’ve been skeptical about learning a whole new programming/coding language “just to make plots”, as I first thought. I have always used statistical programs such as SPSS or Prism to do my plots and they were so easy to work with. However, there is a lot more we can do in R than “just plots”. Also, it is not just because something seems hard that you won’t even try. We need to expose ourselves sometimes. So, I decided to give it a try (and I am proud of myself I did), and here are some of the results:

 

Plot 1: Body Area Index (BAI) vs Day of the Year (DOY)

 

In this plot, we wanted to assess the annual Body Area Index (BAI) trends that describe how skinny (low number) or fat (higher number) a whale is. BAI is a simplified version of the BMI (Body Mass Index) used for humans. If you are interested about this method we have developed at our lab in collaboration with the Aerial Information Systems Laboratory/OSU, you can read more about it in our publication.

The plots above are three versions of the same data displayed in different ways. The first plot on the left shows all the data points by year, with polynomial best fit lines, and the confidence intervals (in gray). There are many overlapping observation points, so for the middle plot I tried to “clean up the plot” by reducing the size of the points and taking out the gray confidence interval range around the lines. In the last plot on the right, I used a linear regression best fit line, instead of polynomial.

We can see a general trend that the BAI was considerably higher in 2016 (red line), when compared to the following years, which makes us question the accuracy of the dataset for that year. In 2016, we also didn’t sample in the month of July, which is causing the 2016 polynomial line to show a sharp decrease in this month (DOY: ~200-230). But it is also interesting to note that the increasing slope of the linear regression line in all three years is very similar, indicating that the whales gained weight at about the same rate in all years.

 

Plot 2: Body Area Index (BAI) vs Body Condition Score (BCS)

 

In addition to the photogrammetry method of assessing whale body condition, we have also performed a body condition scoring method for all the photos we have taken in the field (based on the method described by Bradford et al. 2012). Thus, with this second set of plots, we wanted to compare both methods of assessing whale body condition in order to evaluate when the methods agree or not, and which method would be best and in which situation. Our hypothesis was that whales with a ‘fair’ body condition would have a lower BAI than whales with a ‘good’ body condition.

The plots above illustrate two versions of the same data, with data in the left plot grouped by year, and the data in the right plot grouped by month. In general, we see that no whales were observed with a poor body condition in the last analysis months (August to October), with both methods agreeing to this fact. Additionally, there were many whales that still had a fair body condition in August and September, but less whales in the month of October, indicating that most whales gained weight over the foraging seasons and were ready to start their Southbound migration and another fasting period. This result is important information regarding monitoring and conservation issues.

However, the 2016 dataset is still a concern, since the whales appear to have considerable higher body condition (BAI) when compared to other years.

 

Plot 3:Temporal Body Area Index (BAI) for individual whales

 

In this last group of plots, we wanted to visualize BAI trends over the season (using day of year – DOY) on the x-axis) for individuals we measured more than once. Here we can see the temporal patterns for the whales “Bit”, “Clouds”, “Pearl”, “Scarback, “Pointy”, and “White Hole”.

We expected to see an overall gradual increase in body condition (BAI) over the seasons, such as what we can observe for Pointy in 2018. However, some whales decreased their condition, such as Bit in 2018. Could this trend be accurate? Furthermore, what about BAI measurements that are different from the trend, such as Scarback in 2017, where the last observation point shows a lower BAI than past observation points? In addition, we still observe a high BAI in 2016 at this individual level, when compared to the other years.

My next step will be to check the whole dataset again and search for inconsistencies. There is something causing these 2016 values to possibly be wrong and I need to find out what it is. The overall quality of the measured photogrammetry images was good and in focus, but other variables could be influencing the quality and accuracy of the measurements.

For instance, when measuring images, I often struggled with glare, water splash, water turbidity, ocean swell, and shadows, as you can see in the photos below. All of these variables caused the borders of the whale body to not be clearly visible/identifiable, which may have caused measurements to be wrong.

 

Examples of bad conditions for performing photogrammetry: (1) glare and water splash, (2) water turbidity, (3) ocean swell, and (4) a shadow created in one of the sides of the whale body.
Source: GEMM Lab. Taken under NMFS permit 16111 issued to John Calambokidis.

 

Thus, I will need to check all of these variables to identify the causes for bad measurements and “clean the dataset”. Only after this process will I be able to make these plots again to look at the trends (which will be easy since I already have my R code written!). Then I’ll move on to my next hypothesis that the BAI of individual whales varied by demographics including sex, age and reproductive state.

To carry out robust science that produces results we can trust, we can’t simply collect data, perform a basic analysis, create plots and believe everything we see. Data is often messy, especially when developing new methods like we have done here with drone based photogrammetry and the BAI. So, I need to spend some important time checking my data for accuracy and examining confounding variables that might affect the dataset. Science can be challenging, both when interpreting data or learning a new command language, but it is all worth it in the end when we produce results we know we can trust.

 

 

 

Understanding sea otter effects through complexity

By Dominique Kone, Masters Student in Marine Resource Management

Species reintroductions are a management strategy to augment the reestablishment or recovery of a locally-extinct or extirpated species into once native habitat. The potential for reestablishment success often depends on the species’ ecological characteristics, habitat requirements, and relationship and effects to other species in the environment[1]. While the science behind species reintroductions is continuously evolving and improving, reintroductions are still inherently risky and uncertain in nature. Therefore, every effort should be made to fully assess ecological factors before a reintroduction takes place. As Oregon considers a potential sea otter reintroduction, understanding these ecological factors is an important piece of my own graduate research.

Sea otters are oftentimes referred to as keystone species because they can have wide-reaching effects on the community structure and function of nearshore marine environments. Furthermore, relative to other marine mammals or top predators, several papers have documented these effects – partially due to the ease in observing their foraging and social behaviors, which typically take place close to shore. In many of these studies, a classic paradigm repeatedly appears: when sea otters are present, prey densities (e.g., sea urchins) are significantly reduced, while macroalgae (e.g., kelp, seagrass) densities are high.

Source: Belleza.

While this paradigm is widely-accepted amongst researchers, a few key studies have also demonstrated that the effects of sea otters may be more variable than we once thought. The paradigm does not necessarily hold true everywhere sea otters exist, or at least not to the same degree. For example, after observing benthic communities along islands with varying sea otter densities in the Aleutian archipelago, Alaska, researchers found that islands with abundant otter populations consistently supported low sea urchin densities and high, yet variable, kelp densities. In contrast, islands without otters consistently had low kelp densities and high, yet variable, urchin densities[2]. This study demonstrates that while the classic paradigm generally held true, the degree to which the ecosystem belonged to one of two dominant states (sea otters, low urchins, and high kelp or no sea otters, high urchins, and low kelp) was less obvious.

This example demonstrates the danger in applying this one-size-fits-all paradigm to sea otter effects. Hence, we want to achieve a better understanding of potential sea otter effects so that managers may anticipate how Oregon’s nearshore environments may be affected if sea otters were to be reintroduced. Yet, how can we accurately anticipate these effects given these potential variations and deviations from the paradigm? Interestingly, if we look to other fields outside ecology, we find a possible solution and tool for tackling these uncertainties: a systematic review of available literature.

Two ecosystem states as predicted by the classic paradigm (left: kelp-dominated; right: urchin-dominated). Source: SeaOtters.com.

For decades, medical researchers have been conducting systematic reviews to assess the efficacy of treatments and drugs by combining several studies to find common findings[3]. These findings can then be used to determine any potential variation between studies (i.e. instances where the results may conflict or differ from one another) and even test the influence and importance of key factors that may be driving that variation[4]. While systematic reviews are quite popular within the medical research field, they have not been applied regularly in ecology, but recognition of their application to ecological questions is growing[5]. In our case of achieving a better understanding of the drivers of ecological impacts of sea otter, a systematic literature review is an ideal tool to assess variable effects. This review will be the focus of my second thesis chapter.

In conducting my review, there will be three distinct phases: (1) review design and study collection, (2) meta-analysis, and (3) factor testing. In the first phase (review design and study collection), I will search the existing literature to collect studies that explicitly compare the availability of key ecosystem components (i.e. prey species, non-prey species, and macroalgae species) when sea otters are absent and present in the environment. By only including studies that make this comparison, I will define effects as the proportional change in each species’ or organism group’s availability (e.g. abundance, biomass, density, etc.) with and without sea otters. In determining these effects, it’s important to recognize that sea otters alter ecosystems via both direct and indirect pathways. Direct effects can be thought of as any change to prey availability via sea otter predation directly, while indirect effects can be thought of an any alteration to the broader ecosystem (i.e. non-prey species, macroalgae, habitat features) as an indirect result from sea otter predation on prey species. I will record both types of effects.

General schematic of a meta-analysis in a systematic review. A meta-analysis is the process of taking multiple datasets (i.e. Data 1, Data 2 etc.) from literature sources, calculating summary statistics or effects (i.e. Summary 1, Summary 2, etc.) for each dataset, running statistical procedures (e.g. SMA = sequential meta-analysis) to relate summary effects and investigate between study variation, and identifying important features driving variation. Source: MediCeption.

In phase two, I will use meta-analytical procedures (i.e. statistical analyses specific to systematic reviews) to calculate one standardized metric to represent sea otter effects. These effects will be calculated and averaged across all collected studies. As previously discussed, there may be key factors – such as sea otter density – that influence these effects. Therefore, in phase three (factor testing), effects will also be calculated separately for each a priori factor to test their influence on the effects. Such factors may include habitat type (i.e. hard or soft sediment), prey species (i.e. sea urchins, crabs, clams, etc.), otter density, depth, or time after otter recolonization.

In statistical terms, the goal of testing factors is to see if the variation between studies is impacted by calculating sea otter effects separately for each factor versus across all studies. In other words, if we find high variation in effects between studies, there may be important factors driving that variation. Therefore, in systematic reviews, we recalculate effects separately for each factor to try to explain that variation. If, however, after testing these factors, variation remains high, there may be other factors that we didn’t test that could be driving that remaining variation. Yet, without a priori knowledge on what those factors could be, such variation should be reported as a major source of uncertainty.

Source: Giancarlo Thomae.

Predicting or anticipating the effects of reintroduced species is no easy feat. In instances where the ecological role of a species is well known – and there is adequate data – researchers can develop and use ecosystem models to predict with some certainty what these effects may be. Yet, in other cases where the species’ role is less studied, has less data, or is more variable, researchers must look to other tools – such as systematic reviews – to gain a better understanding of these potential effects. In this case, a systematic review on sea otter effects may prove particularly useful in helping managers understand what types of ecological effects of sea otters in Oregon are most likely, what the important factors are, and, after such review, what we still don’t know about these effects.

References:

[1] Seddon, P. J., Armstrong, D. P., and R. F. Maloney. 2007. Developing the science of reintroduction biology. Conservation Biology. 21(2): 303-312.

[2] Estes, J. A., Tinker, M. T., and J. L. Bodkin. 2009. Using ecological function to develop recovery criteria for depleted species: sea otters and kelp forests in the Aleutian Archipelago. Conservation Biology. 24(3): 852-860.

[3] Sutton, A. J., and J. P. T. Higgins. 2008. Recent developments in meta-analysis. Statistics in Medicine. 27: 625-650.

[4] Arnqvist, G., and D. Wooster. 1995. Meta-analysis: synthesizing research findings in ecology and evolution. TREE. 10(6): 236-240.

[5] Vetter, D., Rucker, G., and I. Storch. 2013. Meta-analysis: a need for well-defined usage in ecology and conservation biology. Ecosphere. 4(6): 1-13.